Molecular Landscape of Tourette's Disorder.

Tourette's disorder (TD) is a highly heritable childhood-onset neurodevelopmental disorder and is caused by a complex interplay of multiple genetic and environmental factors. Yet, the molecular mechanisms underlying the disorder remain largely elusive. In this study, we used the available omics data to compile a list of TD candidate genes, and we subsequently conducted tissue/cell type specificity and functional enrichment analyses of this list. Using genomic data, we also investigated genetic sharing between TD and blood and cerebrospinal fluid (CSF) metabolite levels. Lastly, we built a molecular landscape of TD through integrating the results from these analyses with an extensive literature search to identify the interactions between the TD candidate genes/proteins and metabolites. We found evidence for an enriched expression of the TD candidate genes in four brain regions and the pituitary. The functional enrichment analyses implicated two pathways ('cAMP-mediated signaling' and 'Endocannabinoid Neuronal Synapse Pathway') and multiple biological functions related to brain development and synaptic transmission in TD etiology. Furthermore, we found genetic sharing between TD and the blood and CSF levels of 39 metabolites. The landscape of TD not only provides insights into the (altered) molecular processes that underlie the disease but, through the identification of potential drug targets (such as FLT3, NAALAD2, CX3CL1-CX3CR1, OPRM1, and HRH2), it also yields clues for developing novel TD treatments.

Molecular Landscape of Pelvic Organ Prolapse Provides Insights into Disease Etiology.

Pelvic organ prolapse (POP) represents a major health care burden in women, but its underlying pathophysiological mechanisms have not been elucidated. We first used a case-control design to perform an exome chip study in 526 women with POP and 960 control women to identify single nucleotide variants (SNVs) associated with the disease. We then integrated the functional interactions between the POP candidate proteins derived from the exome chip study and other POP candidate molecules into a molecular landscape. We found significant associations between POP and SNVs in 54 genes. The proteins encoded by 26 of these genes fit into the molecular landscape, together with 43 other POP candidate molecules. The POP landscape is located in and around epithelial cells and fibroblasts of the urogenital tract and harbors four interacting biological processes-epithelial-mesenchymal transition, immune response, modulation of the extracellular matrix, and fibroblast function-that are regulated by sex hormones and TGFB1. Our findings were corroborated by enrichment analyses of differential gene expression data from an independent POP cohort. Lastly, based on the landscape and using vaginal fibroblasts from women with POP, we predicted and showed that metformin alters gene expression in these fibroblasts in a beneficial direction. In conclusion, our integrated molecular landscape of POP provides insights into the biological processes underlying the disease and clues towards novel treatments.

Whole Exome Sequencing in Multi-Incident Families Identifies Novel Candidate Genes for Multiple Sclerosis.

Multiple sclerosis (MS) is a degenerative disease of the central nervous system in which auto-immunity-induced demyelination occurs. MS is thought to be caused by a complex interplay of environmental and genetic risk factors. While most genetic studies have focused on identifying common genetic variants for MS through genome-wide association studies, the objective of the present study was to identify rare genetic variants contributing to MS susceptibility. We used whole exome sequencing (WES) followed by co-segregation analyses in nine multi-incident families with two to four affected individuals. WES was performed in 31 family members with and without MS. After applying a suite of selection criteria, co-segregation analyses for a number of rare variants selected from the WES results were performed, adding 24 family members. This approach resulted in 12 exonic rare variants that showed acceptable co-segregation with MS within the nine families, implicating the genes MBP, PLK1, MECP2, MTMR7, TOX3, CPT1A, SORCS1, TRIM66, ITPR3, TTC28, CACNA1F, and PRAM1. Of these, three genes (MBP, MECP2, and CPT1A) have been previously reported as carrying MS-related rare variants. Six additional genes (MTMR7, TOX3, SORCS1, ITPR3, TTC28, and PRAM1) have also been implicated in MS through common genetic variants. The proteins encoded by all twelve genes containing rare variants interact in a molecular framework that points to biological processes involved in (de-/re-)myelination and auto-immunity. Our approach provides clues to possible molecular mechanisms underlying MS that should be studied further in cellular and/or animal models.

RICOPILI: Rapid Imputation for COnsortias PIpeLIne.

SUMMARY: Genome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an open-sourced Perl-based pipeline was developed to address the challenges of rapidly processing large-scale multi-cohort GWAS studies including quality control (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC in case-control and trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis, (vi) polygenic risk scoring and (vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work. AVAILABILITY AND IMPLEMENTATION: RICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Local patterns of genetic sharing challenge the boundaries between neuropsychiatric and insulin resistance-related conditions.

The co-occurrence of insulin resistance (IR)-related metabolic conditions with neuropsychiatric disorders is a complex public health challenge. Evidence of the genetic links between these phenotypes is emerging, but little is currently known about the genomic regions and biological functions that are involved. To address this, we performed Local Analysis of [co]Variant Association (LAVA) using large-scale (N=9,725-933,970) genome-wide association studies (GWASs) results for three IR-related conditions (type 2 diabetes mellitus, obesity, and metabolic syndrome) and nine neuropsychiatric disorders. Subsequently, positional and expression quantitative trait locus (eQTL)-based gene mapping and downstream functional genomic analyses were performed on the significant loci. Patterns of negative and positive local genetic correlations (|rg|=0.21-1, pFDR<0.05) were identified at 109 unique genomic regions across all phenotype pairs. Local correlations emerged even in the absence of global genetic correlations between IR-related conditions and Alzheimer's disease, bipolar disorder, and Tourette's syndrome. Genes mapped to the correlated regions showed enrichment in biological pathways integral to immune-inflammatory function, vesicle trafficking, insulin signalling, oxygen transport, and lipid metabolism. Colocalisation analyses further prioritised 10 genetically correlated regions for likely harbouring shared causal variants, displaying high deleterious or regulatory potential. These variants were found within or in close proximity to genes, such as SLC39A8 and HLA-DRB1, that can be targeted by supplements and already known drugs, including omega-3/6 fatty acids, immunomodulatory, antihypertensive, and cholesterol-lowering drugs. Overall, our findings underscore the complex genetic landscape of IR-neuropsychiatric multimorbidity, advocating for an integrated disease model and offering novel insights for research and treatment strategies in this domain.

Insulinopathies of the brain? Genetic overlap between somatic insulin-related and neuropsychiatric disorders.

The prevalence of somatic insulinopathies, like metabolic syndrome (MetS), obesity, and type 2 diabetes mellitus (T2DM), is higher in Alzheimer's disease (AD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD). Dysregulation of insulin signalling has been implicated in these neuropsychiatric disorders, and shared genetic factors might partly underlie this observed multimorbidity. We investigated the genetic overlap between AD, ASD, and OCD with MetS, obesity, and T2DM by estimating pairwise global genetic correlations using the summary statistics of the largest available genome-wide association studies for these phenotypes. Having tested these hypotheses, other potential brain "insulinopathies" were also explored by estimating the genetic relationship of six additional neuropsychiatric disorders with nine insulin-related diseases/traits. Stratified covariance analyses were then performed to investigate the contribution of insulin-related gene sets. Significant negative genetic correlations were found between OCD and MetS (rg = -0.315, p = 3.9 × 10-8), OCD and obesity (rg = -0.379, p = 3.4 × 10-5), and OCD and T2DM (rg = -0.172, p = 3 × 10-4). Significant genetic correlations with insulin-related phenotypes were also found for anorexia nervosa (AN), attention-deficit/hyperactivity disorder (ADHD), major depressive disorder, and schizophrenia (p < 6.17 × 10-4). Stratified analyses showed negative genetic covariances between AD, ASD, OCD, ADHD, AN, bipolar disorder, schizophrenia and somatic insulinopathies through gene sets related to insulin signalling and insulin receptor recycling, and positive genetic covariances between AN and T2DM, as well as ADHD and MetS through gene sets related to insulin processing/secretion (p < 2.06 × 10-4). Overall, our findings suggest the existence of two clusters of neuropsychiatric disorders, in which the genetics of insulin-related diseases/traits may exert divergent pleiotropic effects. These results represent a starting point for a new research line on "insulinopathies" of the brain.

Potential role for immune-related genes in autism spectrum disorders: Evidence from genome-wide association meta-analysis of autistic traits.

LAY ABSTRACT: Autism spectrum disorders are complex, with a strong genetic basis. Genetic research in autism spectrum disorders is limited by the fact that these disorders are largely heterogeneous so that patients are variable in their clinical presentations. To address this limitation, we investigated the genetics of individual dimensions of the autism spectrum disorder phenotypes, or autistic-like traits. These autistic-like traits are continuous variations in autistic behaviours that occur in the general population. Therefore, we meta-analysed data from four different population cohorts in which autistic-like traits were measured. We performed a set of genetic analyses to identify common variants for autistic-like traits, understand how these variants related to autism spectrum disorders, and how they contribute to neurobiological processes. Our results showed genetic associations with specific autistic-like traits and a link to the immune system. We offer an example of the potential to use a dimensional approach when dealing with heterogeneous, complex disorder like autism spectrum disorder. Decomposing the complex autism spectrum disorder phenotype in its core features can inform on the specific biology of these features which is likely to account to clinical variability in patients.

Genome-wide analyses of platinum-induced ototoxicity in childhood cancer patients: Results of GO-CAT and United Kingdom MAGIC consortia.

Hearing loss (ototoxicity) is a major adverse effect of cisplatin and carboplatin chemotherapy. The aim of this study is to identify novel genetic variants that play a role in platinum-induced ototoxicity. Therefore, a genome-wide association study was performed in the Genetics of Childhood Cancer Treatment (GO-CAT) cohort (n = 261) and the United Kingdom Molecular Genetics of Adverse Drug Reactions in Children Study (United Kingdom MAGIC) cohort (n = 248). Results of both cohorts were combined in a meta-analysis. In primary analysis, patients with SIOP Boston Ototoxicity Scale grade ≥1 were considered cases, and patients with grade 0 were controls. Variants with a p-value <10-5 were replicated in previously published data by the PanCareLIFE cohort (n = 390). No genome-wide significant associations were found, but variants in TSPAN5, RBBP4P5, AC010090.1 and RNU6-38P were suggestively associated with platinum-induced ototoxicity. The lowest p-value was found for rs7671702 in TSPAN5 (odds ratio 2.0 (95% confidence interval 1.5-2.7), p-value 5.0 × 10-7). None of the associations were significant in the replication cohort, although the effect directions were consistent among all cohorts. Validation and functional understanding of these genetic variants could lead to more insights in the development of platinum-induced ototoxicity.

Shared Genetic Etiology between Alzheimer's Disease and Blood Levels of Specific Cytokines and Growth Factors.

Late-onset Alzheimer's disease (AD) has a significant genetic and immunological component, but the molecular mechanisms through which genetic and immunity-related risk factors and their interplay contribute to AD pathogenesis are unclear. Therefore, we screened for genetic sharing between AD and the blood levels of a set of cytokines and growth factors to elucidate how the polygenic architecture of AD affects immune marker profiles. For this, we retrieved summary statistics from Finnish genome-wide association studies of AD and 41 immune marker blood levels and assessed for shared genetic etiology, using a polygenic risk score-based approach. For the blood levels of 15 cytokines and growth factors, we identified genetic sharing with AD. We also found positive and negative genetic concordances-implying that genetic risk factors for AD are associated with higher and lower blood levels-for several immune markers and were able to relate some of these results to the literature. Our results imply that genetic risk factors for AD also affect specific immune marker levels, which may be leveraged to develop novel treatment strategies for AD.

Genetic overlap between Alzheimer's disease and blood lipid levels.

Late-onset Alzheimer's disease (AD) has a significant genetic component, but the molecular mechanisms through which genetic risk factors contribute to AD pathogenesis are unclear. We screened for genetic sharing between AD and the blood levels of 615 metabolites to elucidate how the polygenic architecture of AD affects metabolomic profiles. We retrieved summary statistics from genome-wide association studies of AD and the metabolite blood levels and assessed for shared genetic etiology, using a polygenic risk score-based approach. For the blood levels of 31 metabolites, all of which were lipids, we identified and replicated genetic sharing with AD. We also found a positive genetic concordance - implying that genetic risk factors for AD are associated with higher blood levels - for 16 of the 31 replicated metabolites. In the brain, lipids and their intermediate metabolites have essential structural and functional roles, such as forming and dynamically regulating synaptic membranes. Our results imply that genetic risk factors for AD affect lipid levels, which may be leveraged to develop novel treatment strategies for AD.

Shared genetic etiology between Parkinson's disease and blood levels of specific lipids.

Parkinson's disease (PD) is characterized by the degeneration of dopaminergic neurons in the substantia nigra and the formation of Lewy bodies. The mechanisms underlying these molecular and cellular effects are largely unknown. Previously, based on genetic and other data, we built a molecular landscape of PD that highlighted a central role for lipids. To explore which lipid species may be involved in PD pathology, we used published genome-wide association study (GWAS) data to conduct polygenic risk score-based analyses to examine putative genetic sharing between PD and blood levels of 370 lipid species and lipid-related molecules. We found a shared genetic etiology between PD and blood levels of 25 lipids. We then used data from a much-extended GWAS of PD to try and corroborate our findings. Across both analyses, we found genetic overlap between PD and blood levels of eight lipid species, namely two polyunsaturated fatty acids (PUFA 20:3n3-n6 and 20:4n6), four triacylglycerols (TAG 44:1, 46:1, 46:2, and 48:0), phosphatidylcholine aa 32:3 (PC aa 32:3) and sphingomyelin 26:0 (SM 26:0). Analysis of the concordance-the agreement in genetic variant effect directions across two traits-revealed a significant negative concordance between PD and blood levels of the four triacylglycerols and PC aa 32:3 and a positive concordance between PD and blood levels of both PUFA and SM 26:0. Taken together, our analyses imply that genetic variants associated with PD modulate blood levels of a specific set of lipid species supporting a key role of these lipids in PD etiology.

Genetic underpinnings of sociability in the general population.

Levels of sociability are continuously distributed in the general population, and decreased sociability represents an early manifestation of several brain disorders. Here, we investigated the genetic underpinnings of sociability in the population. We performed a genome-wide association study (GWAS) of a sociability score based on four social functioning-related self-report questions from 342,461 adults in the UK Biobank. Subsequently we performed gene-wide and functional follow-up analyses. Robustness analyses were performed in the form of GWAS split-half validation analyses, as well as analyses excluding neuropsychiatric cases. Using genetic correlation analyses as well as polygenic risk score analyses we investigated genetic links of our sociability score to brain disorders and social behavior outcomes. Individuals with autism spectrum disorders, bipolar disorder, depression, and schizophrenia had a lower sociability score. The score was significantly heritable (SNP h2 of 6%). We identified 18 independent loci and 56 gene-wide significant genes, including genes like ARNTL, DRD2, and ELAVL2. Many associated variants are thought to have deleterious effects on gene products and our results were robust. The sociability score showed negative genetic correlations with autism spectrum, disorders, depression, schizophrenia, and two sociability-related traits-loneliness and social anxiety-but not with bipolar disorder or Alzheimer's disease. Polygenic risk scores of our sociability GWAS were associated with social behavior outcomes within individuals with bipolar disorder and with major depressive disorder. Variation in population sociability scores has a genetic component, which is relevant to several psychiatric disorders. Our findings provide clues towards biological pathways underlying sociability.

Social and non-social autism symptoms and trait domains are genetically dissociable.

The core diagnostic criteria for autism comprise two symptom domains - social and communication difficulties, and unusually repetitive and restricted behaviour, interests and activities. There is some evidence to suggest that these two domains are dissociable, though this hypothesis has not yet been tested using molecular genetics. We test this using a genome-wide association study (N = 51,564) of a non-social trait related to autism, systemising, defined as the drive to analyse and build systems. We demonstrate that systemising is heritable and genetically correlated with autism. In contrast, we do not identify significant genetic correlations between social autistic traits and systemising. Supporting this, polygenic scores for systemising are significantly and positively associated with restricted and repetitive behaviour but not with social difficulties in autistic individuals. These findings strongly suggest that the two core domains of autism are genetically dissociable, and point at how to fractionate the genetics of autism.